One challenge in utilizing knowledge graphs, especially with machine learning techniques, is the issue of scalability. In this context, we propose a method to substantially reduce the size of these graphs, allowing us to concentrate on the most relevant sections of the graph for a specific application or context. We define the notion of context graph as an extract from one or more general knowledge bases (such as DBpedia, Wikidata, Yago) that contains the set of information relevant to a specific domain while preserving the properties of the original graph. We validate the approach on a DBpedia excerpt for entities related to the Data&Musée project and the KORE reference set according to two aspects: the coverage of the context graph and the preservation of the similarity between its entities. The results show that the use of context graphs makes the exploitation of large knowledge bases more manageable and efficient while preserving the features of the initial graph.